12:58
Why Recurrent Neural Networks Suffer from Vanishing Gradients - Part 1
DataMListic
4:02
Why Convolutional Neural Networks Are Not Permuation Invariant
3:30
Why Weight Regularization Reduces Overfitting
2:42
Why We Need Activation Functions In Neural Networks
11:34
Why Minimizing the Negative Log Likelihood (NLL) Is Equivalent to Minimizing the KL-Divergence
3:44
Why Recurrent Neural Networks (RNN) Suffer from Vanishing Gradients - Part 2
12:24
Why The Reset Gate is Necessary in GRUs
9:01
Why ReLU Is Better Than Other Activation Functions | Tanh Saturating Gradients
7:55
SVM - Large margin classifier?
5:37
Deep by Design: Why Depth Matters in Neural Networks
5:40
Why Naive Bayes Is Naive
4:58
Why Residual Connections (ResNet) Work
2:21
Why We Don't Accept The Null Hypothesis
4:29
Why Neural Networks Can Learn Any Function
5:32
Why we perform feature normalization in ML
8:19
AMSGrad - Why Adam FAILS to Converge
Why Deep Neural Networks (DNNs) Underperform Tree-Based Models on Tabular Data
4:47
Bias-Variance Trade-off - Explained
6:21
Why We Divide by N-1 in the Sample Variance (The Bessel's Correction)
5:55
Why LLMs Hallucinate
8:52
Why We Don't Use the Mean Squared Error (MSE) Loss in Classification
6:06
Capsule Networks Explained
3:56
Why Batch Normalization (batchnorm) Works
2:37
Why L1 Regularization Produces Sparse Weights (Geometric Intuition)
